Overview of Cilantro-SL

Cilantro-SL is a two-stage framework for predicting SL gene pairs that decouples viability-aware single-gene KO representation learning from pairwise SL classification with conformal calibration for rigorous uncertainty quantification. This separation allows the model to first learn transferable perturbation-aware representations and then reuse them flexibly for generalization tasks.

1. Viability pretraining: Utilizes a scFM, Geneformer, to generate perturbed and unperturbed embeddings for (cell x gene) combinations to construct a delta embedding. The delta embedding is then conditioned on a gene embedding through feature-wise linear modulation (FiLM) to predict gene knockout viability, creating a viability embedding.
2. SL classification: Takes in a pair of viability embeddings to predict SL/non-SL relationships with calibrated uncertainty measurements through conformal prediction. Classifier scores are converted into calibrated outputs via conformal prediction with corresponding confidence scores, yielding high-confidence SL candidates for experimental testing.

Running Cilantro-SL

Input data

Cilantro-SL requires viability scores for each (cell x gene) KO combination as well as bulk RNA-seq data for each cell line. In addition to this, Cilantro-SL can take in an additional gene prior to condition on, such as Gene2vec embeddings.

1. The viability scores and RNA-seq data should be two pandas DataFrames with cell lines as the index and gene ensembl IDs as the column
2. The gene prior should be a mapping from ensembl IDs to a gene embedding, and will be concatenated with a DataFrame of viability embeddings

Installation

Step 1: Create a Conda Environment

We recommend using Anaconda to manage your environment. If you haven't already, refer to the Anaconda webpage for installation instructions.

Create a Python 3.12.4 environment using the following command:

conda env create -f cilantro_sl.yml

Activate the environment:

conda activate cilantro_sl

Running the code

The bulk RNA-seq data must be tokenized into a 2048-length rank-value encoding via Geneformer.

To run Cilantro-SL, follow the notebooks in sequence stored in notebooks. This will tokenize the data, perform knockouts to generate embeddings using Geneformer, and compile the dataframe necessary for Cilantro-SL. Afterwards, Cilantro-SL can be run using the pretraining notebook and the classification notebook, which contains the uncertainty quantification code.

The modified Geneformer can be found here in the geneformer directory. The torch_batch_dir should be changed to your filepath.